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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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  {
   "source": [
    "## How to use this Notebook\n",
    "The development cycle intended here is to \n",
    "\n",
    "1. Init an experiment\n",
    "\n",
    "2. Reset the model and optimizer\n",
    "\n",
    "3. Restart wandb\n",
    "\n",
    "4. Run one epoch\n",
    "\n",
    "5. Add any new logs for plots, as needed\n",
    "\n",
    "Then repeat steps 4-5 as needed or 2-5 as needed.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "source": [
    "## Initialize the experiment"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "INFO:DendritesExperiment:Execution order: {'adapt': ['MetaContinualLearningExperiment.adapt'],\n",
      " 'clone_model': ['MetaContinualLearningExperiment.clone_model'],\n",
      " 'complexity_loss': [],\n",
      " 'create_fast_slow_loaders': ['MetaContinualLearningExperiment.create_fast_slow_loaders'],\n",
      " 'create_loaders': ['MetaContinualLearningExperiment.create_loaders',\n",
      "                    'Create loaders for the meta testing phase'],\n",
      " 'create_model': ['SupervisedExperiment.create_model', 'RezeroWeights'],\n",
      " 'create_replay_sampler': ['MetaContinualLearningExperiment.create_replay_sampler'],\n",
      " 'create_sampler': ['MetaContinualLearningExperiment.create_sampler'],\n",
      " 'create_slow_train_dataloader': ['MetaContinualLearningExperiment.create_slow_train_dataloader'],\n",
      " 'create_task_sampler': ['MetaContinualLearningExperiment.create_task_sampler'],\n",
      " 'create_train_dataloader': ['SupervisedExperiment.create_train_dataloader'],\n",
      " 'create_train_sampler': ['MetaContinualLearningExperiment.create_train_sampler'],\n",
      " 'create_validation_dataloader': ['SupervisedExperiment.create_validation_dataloader'],\n",
      " 'create_validation_sampler': ['MetaContinualLearningExperiment.create_validation_sampler'],\n",
      " 'error_loss': ['SupervisedExperiment.error_loss'],\n",
      " 'get_model': ['MetaContinualLearningExperiment.get_model'],\n",
      " 'get_named_fast_params': ['MetaContinualLearningExperiment.get_named_fast_params'],\n",
      " 'get_readable_result': ['SupervisedExperiment: Basic keys'],\n",
      " 'get_state': ['SupervisedExperiment: Model, optimizer, LR scheduler, epoch'],\n",
      " 'insert_pre_experiment_result': [],\n",
      " 'load_dataset': ['SupervisedExperiment.load_dataset'],\n",
      " 'post_batch': ['SupervisedExperiment: Logging', 'RezeroWeights'],\n",
      " 'post_epoch': ['Run meta testing phase', 'RezeroWeights logging'],\n",
      " 'pre_batch': [],\n",
      " 'pre_epoch': [],\n",
      " 'pre_task': ['MetaContinualLearningExperiment.pre_task',\n",
      "              'Reset the output params for upcoming tasks.'],\n",
      " 'run_epoch': ['MetaContinualLearningExperiment.run_epoch'],\n",
      " 'run_iteration': ['SupervisedExperiment.run_iteration'],\n",
      " 'run_pre_experiment': ['SupervisedExperiment.run_pre_experiment'],\n",
      " 'run_task': ['MetaContinualLearningExperiment.run_task'],\n",
      " 'set_state': ['SupervisedExperiment: Model, optimizer, LR scheduler, epoch'],\n",
      " 'setup_experiment': ['ExperimentBase: Initialize logger',\n",
      "                      'SupervisedExperiment.setup_experiment',\n",
      "                      'OML meta-testing setup',\n",
      "                      'RezeroWeights logging'],\n",
      " 'should_decay_parameter': ['SupervisedExperiment.should_decay_parameter'],\n",
      " 'should_stop': ['SupervisedExperiment.should_stop'],\n",
      " 'stop_experiment': [],\n",
      " 'train_epoch': ['SupervisedExperiment.train_epoch'],\n",
      " 'transform_data_to_device': ['SupervisedExperiment.transform_data_to_device'],\n",
      " 'transform_model': ['SupervisedExperiment.transform_model'],\n",
      " 'update_params': ['MetaContinualLearningExperiment.update_params'],\n",
      " 'validate': ['SupervisedExperiment.validate']}\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "INFO:DendritesExperiment:Setup: fast_param_names=['classifier.weight', 'classifier.bias', 'prediction.0.weight', 'prediction.0.bias', 'prediction.3.weight', 'prediction.3.bias', 'prediction.6.weight', 'prediction.6.bias']\n",
      "INFO:DendritesExperiment:Setup: test_train_param_names=['classifier.weight', 'classifier.bias']\n",
      "INFO:DendritesExperiment:Setup: output_param_names=[]\n"
     ]
    }
   ],
   "source": [
    "# Load the exp\n",
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.insert(0, os.path.expanduser(\"~/nta/nupic.research/projects/meta_cl\"))\n",
    "\n",
    "from experiments import CONFIGS\n",
    "\n",
    "exp_name = \"metacl_dendrites2\"\n",
    "config = CONFIGS[exp_name]\n",
    "exp_cls = config[\"experiment_class\"]\n",
    "exp = exp_cls()\n",
    "exp.setup_experiment(config)"
   ]
  },
  {
   "source": [
    "## Reset the Model, Optimizer, and Wandb\n",
    "Helpful to start new runs quickly"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset the optimizer and model\n",
    "exp.model.reset_params()\n",
    "\n",
    "# Configure optimizer\n",
    "group_decay, group_no_decay = [], []\n",
    "for module in exp.model.modules():\n",
    "    for name, param in module.named_parameters(recurse=False):\n",
    "        if exp.should_decay_parameter(module, name, param, config):\n",
    "            group_decay.append(param)\n",
    "        else:\n",
    "            group_no_decay.append(param)\n",
    "\n",
    "optimizer_class = config.get(\"optimizer_class\", torch.optim.SGD)\n",
    "optimizer_args = config.get(\"optimizer_args\", {})\n",
    "exp.optimizer = optimizer_class([dict(params=group_decay),\n",
    "                                  dict(params=group_no_decay,\n",
    "                                       weight_decay=0.)],\n",
    "                                 **optimizer_args)\n",
    "\n",
    "# Reset the epoch\n",
    "exp.current_epoch = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Finishing last run (ID:1jffpiab) before initializing another..."
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<br/>Waiting for W&B process to finish, PID 17941<br/>Program ended successfully."
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "bf312a066d1d4635b38e6d9368332294"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Find user logs for this run at: <code>/home/ec2-user/nta/results/wandb/run-20201124_174132-1jffpiab/logs/debug.log</code>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Find internal logs for this run at: <code>/home/ec2-user/nta/results/wandb/run-20201124_174132-1jffpiab/logs/debug-internal.log</code>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "\n                    <br/>Synced <strong style=\"color:#cdcd00\">metacl_dendrites2</strong>: <a href=\"https://wandb.ai/numenta/metacl_dendrites_test/runs/1jffpiab\" target=\"_blank\">https://wandb.ai/numenta/metacl_dendrites_test/runs/1jffpiab</a><br/>\n                "
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "...Successfully finished last run (ID:1jffpiab). Initializing new run:<br/><br/>"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.10.11 is available!  To upgrade, please run:\n\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "\n                Tracking run with wandb version 0.10.10<br/>\n                Syncing run <strong style=\"color:#cdcd00\">metacl_dendrites2</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n                Project page: <a href=\"https://wandb.ai/numenta/metacl_dendrites_test\" target=\"_blank\">https://wandb.ai/numenta/metacl_dendrites_test</a><br/>\n                Run page: <a href=\"https://wandb.ai/numenta/metacl_dendrites_test/runs/3nj6uoy8\" target=\"_blank\">https://wandb.ai/numenta/metacl_dendrites_test/runs/3nj6uoy8</a><br/>\n                Run data is saved locally in <code>/home/ec2-user/nta/results/wandb/run-20201124_174406-3nj6uoy8</code><br/><br/>\n            "
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/html": "<h1>Run(3nj6uoy8)</h1><p></p><iframe src=\"https://wandb.ai/numenta/metacl_dendrites_test/runs/3nj6uoy8\" style=\"border:none;width:100%;height:400px\"></iframe>",
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x7f08a789a890>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "import wandb\n",
    "\n",
    "if wandb.run is not None:\n",
    "    wandb.join()\n",
    "\n",
    "wandb.init(name=\"metacl_dendrites2\", project=\"metacl_dendrites_test\", reinit=True)"
   ]
  },
  {
   "source": [
    "# Run One Epoch\n",
    "\n",
    "Be sure to run **Functions to Run the Model** and **Add Wandb Plots via Hooks** below to define the run functions and desired plots.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[ 7.17166578e-03  1.26597565e-02 -8.31647962e-03 ... -1.12970844e-02\n   2.19237129e-03  1.15099782e-02]\n [ 8.98968987e-03  2.96542747e-03 -1.03747640e-02 ... -3.47367488e-04\n  -5.79400919e-04  7.63060199e-03]\n [ 6.05306635e-03  1.51492134e-02 -1.12439096e-02 ... -8.51327181e-03\n   2.02114228e-03  7.70116039e-03]\n ...\n [-4.46438417e-03  2.18834020e-02 -3.70922498e-05 ...  4.81407018e-03\n  -1.81725398e-02  4.14841063e-03]\n [ 8.79820250e-03  5.70637453e-03 -5.55902021e-03 ... -1.07848831e-02\n   1.97236426e-03 -5.86457178e-03]\n [-4.88775317e-04  1.16086286e-02 -6.36933325e-03 ... -1.70418248e-03\n  -5.02706366e-03 -3.54889268e-03]]\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'total_correct': 20,\n",
       " 'total_tested': 84,\n",
       " 'mean_loss': 9.039420127868652,\n",
       " 'mean_accuracy': 0.23809523809523808,\n",
       " 'learning_rate': 0.0001}"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "run_epoch(exp)"
   ]
  },
  {
   "source": [
    "## Functions to Run the Model\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_epoch(exp):\n",
    "\n",
    "    exp.pre_epoch()\n",
    "\n",
    "    exp.optimizer.zero_grad()\n",
    "\n",
    "    # Sample tasks for inner loop.\n",
    "    tasks_train = np.random.choice(\n",
    "        exp.fast_and_slow_classes,\n",
    "        size=exp.tasks_per_epoch,\n",
    "        replace=False\n",
    "    )\n",
    "\n",
    "    # Run pre_task; For instance, may reset parameters as needed.\n",
    "    exp.pre_task(tasks_train)\n",
    "\n",
    "    # Clone model - clone fast params and the slow params. The latter will be frozen\n",
    "    cloned_adaptation_net = exp.clone_model()\n",
    "\n",
    "    # Inner loop: Train over sampled tasks.\n",
    "    for task in tasks_train:\n",
    "        run_task(exp, task, cloned_adaptation_net)\n",
    "\n",
    "    # Sample from the replay set.\n",
    "    exp.train_replay_loader.sampler.set_active_tasks(exp.replay_classes)\n",
    "    replay_data, replay_target = next(iter(exp.train_replay_loader))\n",
    "\n",
    "    # Sample from the slow set.\n",
    "    slow_data, slow_target = [], []\n",
    "    for task in tasks_train:\n",
    "        exp.train_slow_loader.sampler.set_active_tasks(task)\n",
    "        x, y = next(iter(exp.train_slow_loader))\n",
    "        slow_data.append(x)\n",
    "        slow_target.append(y)\n",
    "\n",
    "    # Concatenate the slow and replay set.\n",
    "    slow_data = torch.cat(slow_data + [replay_data]).to(exp.device)\n",
    "    slow_target = torch.cat(slow_target + [replay_target]).to(exp.device)\n",
    "\n",
    "    # LOGGING\n",
    "    # cloned_adaptation_net.classifier.register_forward_hook(partial(fhook, name=\"classifier\"))\n",
    "    cloned_adaptation_net.apply_dendrites = apply_dendrites_and_log\n",
    "\n",
    "    # Take step for outer loop. This will backprop through to the original\n",
    "    # slow and fast params.\n",
    "    output = cloned_adaptation_net(slow_data)\n",
    "    loss = exp._loss_function(output, slow_target)\n",
    "    loss.backward()\n",
    "\n",
    "    exp.optimizer.step()\n",
    "\n",
    "    # Report statistics for the outer loop\n",
    "    pred = output.max(1, keepdim=True)[1]\n",
    "    correct = pred.eq(slow_target.view_as(pred)).sum().item()\n",
    "    total = output.shape[0]\n",
    "    results = {\n",
    "        \"total_correct\": correct,\n",
    "        \"total_tested\": total,\n",
    "        \"mean_loss\": loss.item(),\n",
    "        \"mean_accuracy\": correct / total if total > 0 else 0,\n",
    "        \"learning_rate\": exp.get_lr()[0],\n",
    "    }\n",
    "    exp.logger.debug(results)\n",
    "\n",
    "    exp.post_epoch()\n",
    "\n",
    "    exp.current_epoch += 1\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_task(exp, task, cloned_adaptation_net):\n",
    "    exp.train_fast_loader.sampler.set_active_tasks(task)\n",
    "\n",
    "    # Meta-train training. Use no more than `num_fast_steps` sequential updates.\n",
    "    for i, (data, target) in enumerate(exp.train_fast_loader):\n",
    "        if i >= exp.num_fast_steps:\n",
    "            break\n",
    "\n",
    "        data = data.to(exp.device)\n",
    "        target = target.to(exp.device)\n",
    "        train_loss = exp._loss_function(\n",
    "            cloned_adaptation_net(data), target\n",
    "        )\n",
    "        # Update in place\n",
    "        exp.adapt(cloned_adaptation_net, train_loss)\n",
    "\n",
    "    # See if there are images to validate on. If 'train_train_sample_size'\n",
    "    # is equivalent to the number of images per class, then there won't be any.\n",
    "    if len(exp.val_fast_loader) == 0:\n",
    "        return\n",
    "\n",
    "    # Run and log validation for given task.\n",
    "    with torch.no_grad():\n",
    "        exp.val_fast_loader.sampler.set_active_tasks(task)\n",
    "\n",
    "        data, target = next(iter(exp.val_fast_loader))\n",
    "        data = data.to(exp.device)\n",
    "        target = target.to(exp.device)\n",
    "\n",
    "        preds = cloned_adaptation_net(data)\n",
    "        valid_error = exp._loss_function(preds, target)\n",
    "        valid_error /= len(data)\n",
    "        exp.logger.debug(f\"Valid error meta train training: {valid_error}\")\n",
    "\n",
    "        # calculate accuracy\n",
    "        preds = preds.argmax(dim=1).view(target.shape)\n",
    "        valid_accuracy = (preds == target).sum().float() / target.size(0)\n",
    "        exp.logger.debug(f\"Valid accuracy meta train training: {valid_accuracy}\")"
   ]
  },
  {
   "source": [
    "## Add Wandb Plots via Hooks"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "```\n",
    "DendriticNetwork2(\n",
    "  (segments): DendriteSegments()\n",
    "  (classifier): Linear(in_features=2304, out_features=963, bias=True)\n",
    "  (prediction): Sequential(\n",
    "    (0): Conv2d(3, 256, kernel_size=(3, 3), stride=(1, 1))\n",
    "    (1): ReLU()\n",
    "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "    (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
    "    (4): ReLU()\n",
    "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "    (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
    "    (7): ReLU()\n",
    "    (8): AdaptiveAvgPool2d(output_size=(3, 3))\n",
    "    (9): Flatten()\n",
    "  )\n",
    "  (modulation): Sequential(\n",
    "    (0): Conv2d(3, 112, kernel_size=(3, 3), stride=(1, 1))\n",
    "    (1): ReLU()\n",
    "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "    (3): Conv2d(112, 112, kernel_size=(3, 3), stride=(1, 1))\n",
    "    (4): ReLU()\n",
    "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "    (6): Conv2d(112, 112, kernel_size=(3, 3), stride=(1, 1))\n",
    "    (7): ReLU()\n",
    "    (8): AdaptiveAvgPool2d(output_size=(3, 3))\n",
    "    (9): Flatten()\n",
    "    (10): Linear(in_features=1008, out_features=100, bias=True)\n",
    "  )\n",
    ")\n",
    "```"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fhook(module, x_tuple, y, name=None):\n",
    "    print(f\"NAME: {name} ({module.__class__.__name__})\")\n",
    "    print(\"  input :\", x_tuple[0].shape)\n",
    "    print(\"  output:\", y.shape)\n",
    "    print()\n",
    "    x = x_tuple[0].clone().detach().cpu().numpy()\n",
    "    wandb.log({\n",
    "        \"pred_modulated_0\": wandb.Histogram(x[0, :]),\n",
    "        \"pred_modulated_20\": wandb.Histogram(x[20, :]),\n",
    "        \"sparsity_pred_modulated_0\": (x[0, :] == 0).sum().item() / len(x[0, :])\n",
    "    })\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_dendrites_and_log(y, dendrite_activations):\n",
    "    \"\"\"Apply dendrites as a gating mechanism.\"\"\"\n",
    "    # # Multiple by the sigmoid of the max along each segment.\n",
    "    # return y * torch.sigmoid(dendrite_activations.max(dim=2).values)\n",
    "\n",
    "    input_dendrite_activations = dendrite_activations.clone()\n",
    "\n",
    "    inds = dendrite_activations.abs().max(dim=2).indices\n",
    "    inds = inds.unsqueeze(dim=2)\n",
    "    dendrite_activations = torch.gather(dendrite_activations, dim=2, index=inds)\n",
    "    dendrite_activations = dendrite_activations.squeeze(dim=2)\n",
    "    dendrite_activations = torch.sigmoid(dendrite_activations)\n",
    "\n",
    "    out = y * dendrite_activations\n",
    "\n",
    "    log_modulated(y, out, input_dendrite_activations)\n",
    "\n",
    "    return out\n",
    "\n",
    "def log_modulated(pre_modulated, post_modulated, input_dendrite_activations):\n",
    "\n",
    "    pre_modulated = to_numpy(pre_modulated)\n",
    "    post_modulated = to_numpy(post_modulated)\n",
    "    input_dendrite_activations = to_numpy(input_dendrite_activations)\n",
    "\n",
    "    print(input_dendrite_activations[:, 0, :])\n",
    "\n",
    "    wandb.log({\n",
    "        # Pre-modulated values\n",
    "        \"pre_modulated_0\": wandb.Histogram(pre_modulated[0, :]),\n",
    "        \"pre_modulated_20\": wandb.Histogram(pre_modulated[20, :]),\n",
    "        \"sparsity_pre_modulated\": (pre_modulated == 0).sum().item() / len(pre_modulated.flatten()),\n",
    "\n",
    "        # Post-modulated values\n",
    "        \"post_modulated_0\": wandb.Histogram(post_modulated[0, :]),\n",
    "        \"post_modulated_20\": wandb.Histogram(post_modulated[20, :]),\n",
    "        \"sparsity_post_modulated\": (post_modulated == 0).sum().item() / len(post_modulated.flatten()),\n",
    "\n",
    "        # Dendrite activations\n",
    "        \"dendrite_activation_unit_0\": plot_dendrite_activations(input_dendrite_activations[:, 0, :]),\n",
    "    })\n",
    "\n",
    "\n",
    "def plot_dendrite_activations(dendrite_activations):\n",
    "    plt.cla()\n",
    "\n",
    "    activations = dendrite_activations.transpose(1, 0)[:, 0:84]\n",
    "\n",
    "    num_contexts = 84\n",
    "    num_dendrites = 4\n",
    "\n",
    "    x_labels = [\n",
    "        \"context {}\".format(j) for j in range(num_contexts)\n",
    "    ]\n",
    "    y_labels = [\"dendrite {}\".format(j) for j in range(num_dendrites)]\n",
    "\n",
    "    # Find the range of activation values to anchor the colorbar\n",
    "    vmax = np.abs(activations).max()\n",
    "    vmin = -1.0 * vmax\n",
    "\n",
    "    # Use matplotlib to plot the activation heatmap\n",
    "    fig, ax = plt.subplots(figsize=(30, 10))\n",
    "    ax.imshow(activations, cmap=\"coolwarm_r\", vmin=vmin, vmax=vmax)\n",
    "\n",
    "    ax.set_xticks(np.arange(num_contexts))\n",
    "    ax.set_yticks(np.arange(num_dendrites))\n",
    "\n",
    "    ax.set_xticklabels(x_labels)\n",
    "    ax.set_yticklabels(y_labels)\n",
    "\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Annotate just the top absolute activation for each context\n",
    "    top_activation_dendrite_per_context = np.argmax(np.abs(activations), axis=0)\n",
    "    for j, i in enumerate(top_activation_dendrite_per_context):\n",
    "        val = np.round(activations[i, j], 2)\n",
    "        ax.text(j, i, val, ha=\"center\", va=\"center\", color=\"w\")\n",
    "\n",
    "    figure = plt.gcf()\n",
    "    return fig\n",
    "\n",
    "def to_numpy(tensor):\n",
    "    return tensor.clone().detach().cpu().numpy()"
   ]
  }
 ]
}